{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 29.000762 seconds\n",
      "One loop version took 63.045612 seconds\n",
      "No loop version took 0.231369 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.526000\n",
      "k = 1, accuracy = 0.514000\n",
      "k = 1, accuracy = 0.528000\n",
      "k = 1, accuracy = 0.556000\n",
      "k = 1, accuracy = 0.532000\n",
      "k = 3, accuracy = 0.482000\n",
      "k = 3, accuracy = 0.498000\n",
      "k = 3, accuracy = 0.486000\n",
      "k = 3, accuracy = 0.546000\n",
      "k = 3, accuracy = 0.528000\n",
      "k = 5, accuracy = 0.512000\n",
      "k = 5, accuracy = 0.542000\n",
      "k = 5, accuracy = 0.560000\n",
      "k = 5, accuracy = 0.578000\n",
      "k = 5, accuracy = 0.556000\n",
      "k = 8, accuracy = 0.526000\n",
      "k = 8, accuracy = 0.574000\n",
      "k = 8, accuracy = 0.552000\n",
      "k = 8, accuracy = 0.576000\n",
      "k = 8, accuracy = 0.540000\n",
      "k = 10, accuracy = 0.532000\n",
      "k = 10, accuracy = 0.592000\n",
      "k = 10, accuracy = 0.558000\n",
      "k = 10, accuracy = 0.566000\n",
      "k = 10, accuracy = 0.566000\n",
      "k = 12, accuracy = 0.522000\n",
      "k = 12, accuracy = 0.588000\n",
      "k = 12, accuracy = 0.560000\n",
      "k = 12, accuracy = 0.566000\n",
      "k = 12, accuracy = 0.560000\n",
      "k = 15, accuracy = 0.506000\n",
      "k = 15, accuracy = 0.580000\n",
      "k = 15, accuracy = 0.558000\n",
      "k = 15, accuracy = 0.560000\n",
      "k = 15, accuracy = 0.550000\n",
      "k = 20, accuracy = 0.540000\n",
      "k = 20, accuracy = 0.558000\n",
      "k = 20, accuracy = 0.558000\n",
      "k = 20, accuracy = 0.560000\n",
      "k = 20, accuracy = 0.568000\n",
      "k = 50, accuracy = 0.542000\n",
      "k = 50, accuracy = 0.576000\n",
      "k = 50, accuracy = 0.556000\n",
      "k = 50, accuracy = 0.538000\n",
      "k = 50, accuracy = 0.532000\n",
      "k = 100, accuracy = 0.512000\n",
      "k = 100, accuracy = 0.540000\n",
      "k = 100, accuracy = 0.526000\n",
      "k = 100, accuracy = 0.512000\n",
      "k = 100, accuracy = 0.526000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "step = int(num_training/num_folds)\n",
    "for k in k_choices:\n",
    "    acc = []\n",
    "    for i in range(num_folds):\n",
    "        train_data = np.concatenate([X_train[:i*step], X_train[(i+1)*step:]], axis = 0)\n",
    "        test_data = X_train[i*step:(i+1)*step]\n",
    "        train_label = np.concatenate([y_train[:i*step], y_train[(i+1)*step:]], axis = 0)\n",
    "        test_label = y_train[i*step:(i+1)*step]\n",
    "\n",
    "        classifier = KNearestNeighbor()\n",
    "        classifier.train(train_data, train_label)\n",
    "        dists = classifier.compute_distances_no_loops(test_data)\n",
    "        label_test_pred = classifier.predict_labels(dists, k=k)\n",
    "        num_correct = np.sum(label_test_pred == test_label)\n",
    "        acc.append(float(num_correct) / num_test)\n",
    "        \n",
    "    k_to_accuracies[k] = acc\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$4\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
